Lung model parameter estimation by unscented Kalman filter

Non-Contact Respiratory Rate Estimation in Real-Time With Modified Joint Unscented Kalman Filter

It can be life-saving to monitor the respiratory rate (RR) even for healthy people in real-time. It is reported that the infected people with coronavirus disease 2019 (COVID-19), generally develop mild respiratory symptoms in the early stage. It will be more important to continuously monitor the RR of people in nursing homes and houses with a non-contact method. Conventional, contact-based, methods are not suitable for long-term health monitoring especially in-home care services. The potentials of wireless radio signals for health care applications, such as fall detection, etc., are examined in literature. In this paper, we focus on a device-free real-time RR monitoring system using wireless signals. In our recent study, we proposed a non-contact RR monitoring system with a batch processing (delayed) estimation method. In this paper, for real-time monitoring, we modify the standard joint unscented Kalman filter (JUKF) method for this new and time-critical problem. Due to the nonlinear structure of the RR estimation problem with respect to the measurements, a novel modification is proposed to transform measurement errors into parameter errors by using the hyperbolic tangent function. It is shown in the experiments conducted with the real measurements taken using healthy volunteers that the proposed modified joint unscented Kalman filter (ModJUKF) method achieves the highest accuracy according to the windowing-based methods in the time-varying RR scenario. It is also shown that the ModJUKF not only reduces the computational complexity approximately 8.54% but also improves the accuracy 36.7% with respect to the standard JUKF method.

Leakage estimation using Kalman filtering in noninvasive mechanical ventilation

Noninvasive mechanical ventilation is today often used to assist patient with chronic respiratory failure. One of the main reasons evoked to explain asynchrony events, discomfort, unwillingness to be treated, etc., is the occurrence of nonintentional leaks in the ventilation circuit, which are difficult to account for because they are not measured. This paper describes a solution to the problem of variable leakage estimation based on a Kalman filter driven by airflow and the pressure signals, both of which are available in the ventilation circuit. The filter was validated by showing that based on the attained leakage estimates, practically all the untriggered cycles can be explained.

Noninvasive method for measuring respiratory system compliance during pressure support ventilation

PURPOSE

To date, few methods have been accepted for assessing the respiratory system compliance (C(rs)) in patients under assisted ventilation at the bedside. The aim of this study was to evaluate our adaptive time slice method (ATSM) to continuously calculate the C(rs).

METHODS

One breath is divided into several time periods (slices). For each slice, a compliance value C(i) is calculated. The slice width is adapted according to the confidence interval of C(i). After all C(i) values are obtained and the outliers are eliminated, the C(rs) of this breath is calculated as the mean value of the remainder of C(i)'s. Seven patients with Chronic Obstructive Pulmonary Disease were evaluated during pressure support ventilation. The results are compared with the values calculated with the transdiaphragmatic pressure (P(di)).

RESULTS

95 ± 4% of the recorded data could be analyzed with ATSM. In 6 patients out of 7, the results delivered with ATSM and with P(di) had similar variation (standard deviation) and accuracy (difference<20%). They were strongly correlated (weighted correlation coefficient = 0.86, p<10(-5)) with a mean difference of 3.22 ml/mbar.

CONCLUSIONS

The ATSM is a robust method and able to provide accurate C(rs) in spontaneously breathing patients during pressure support ventilation noninvasively without extra instrumentation or complicated maneuvers.